Funding Overview

Column

GCF

1,578,814,635

GEF

19,028,636,363

NAPs

21

Column

Column

Readiness

Projects by Region

Amount Approved by Country

Outputs

NAP Outputs

Map

Countries with Submitted NAPs

Temperature Time-series

About

About

The Open NAPs Dashboard

Open NAPs are National Adaptation Plans for developing countries being developed through an open collaboration involving a wide range of actors, designed to support countries develop the best possible adaptation plans along the guidelines established by the UNFCCC Conference of Parties, and further elaborated through technical guidelines led by the LDC Expert Group (LEG), with continuing extension through supplements developed by a wide range of organizations.

The Open NAP initiative is moderated by the LEG under the UNFCCC. For more information about the work of the LEG, visit here

For more information about the National Adaptation Plans, browse here

For any questions or feedback, write to us

---
title: "NAP Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: row
    vertical_layout: fill
    source_code: embed
    
---

```{r setup, include=FALSE,echo=FALSE}
library(flexdashboard)
```


```{r, include=FALSE}
library(RODBC)
library(magrittr)
napsdata<-odbcConnect("Open_NAPs_db")
sqlTables(napsdata)
country_codes_un<-sqlFetch(napsdata,"country_codes_un")
nap_outputs<-sqlFetch(napsdata,"nap_output_docs")%>% na.omit(nap_outputs)
Readiness<-sqlFetch(napsdata,"Readiness")
GEF<-sqlFetch(napsdata,"GEF")%>% na.omit(GEF)
GCF<-sqlFetch(napsdata,"GCF")%>% na.omit(GCF)
NAPS<-sqlFetch(napsdata,"Submitted_NAPs")
latlong<-sqlFetch(napsdata,"countrylatlong")
NAPATbl<-sqlFetch(napsdata,"project_database")
```


```{r, include=FALSE}
library(magrittr)
napcountry_outputs<-merge(country_codes_un,nap_outputs, by="country-code")
GEF_Projects<-merge(country_codes_un,GEF, by="countryname", all.y = TRUE)
GCF_Projects<-merge(country_codes_un,GCF, by="countryname", all.y = TRUE)
Country_Readiness<-merge(country_codes_un,Readiness, by="countryname", all.y = TRUE)
NAPCountries<-merge(country_codes_un,NAPS, by="countryname", all.y = TRUE)
xycountrycode<-merge(country_codes_un,latlong, by='countryname', all.x = TRUE)
NAP_Countries<-merge(NAPS,xycountrycode, by="countryname", all.x = TRUE)
NAPA<-merge(NAPATbl,xycountrycode, by="countryname", all.x = TRUE)
```


```{r,include=FALSE}

library(dplyr)
library(magrittr)
GCF_Core <- as.data.frame(GCF_Projects)%>% group_by(`countryname`)%>% 
  summarise("Total GCF Funding"=sum(`Total GCF Funding`))#%>%
      #tidyr::pivot_wider(names_from = "countryname", values_from ="Total GCF Funding")
GCF_Co_Finance <- as.data.frame(GCF_Projects)%>% group_by(`countryname`)%>% 
  summarise("Total Co-Financing"=sum(`Total Co-Financing`))
GEF_Grant <- as.data.frame(GEF_Projects)%>% group_by(`countryname`)%>% 
  summarise("Grant"=sum(`Grant`))
GEF_Co_Finance <- as.data.frame(GEF_Projects)%>% group_by(`countryname`)%>% 
  summarise("Total Co-Financing"=sum(`Co-Financing`))
Readiness_df <- as.data.frame(Country_Readiness)%>% group_by(`countryname`,Region,`Grant Agreement Type`)%>%   summarise("Amount Approved"=sum(`Amount Approved`),"Amount Disbursed"=sum(`Amount Disbursed`) )
NAPA_df <- as.data.frame(NAPA)%>% group_by(`countryname`,Region,Fund,status)%>%   summarise("Amount Approved"=sum(Amount) )

```
Funding Overview
=======================================================================
Column { data-width=50 }
-----------------------------------------------------------------------
### GCF {.value-box}

-----------------------------------------------------------------------



```{r}
gcf_total<-sum(GCF_Core$`Total GCF Funding`)

valueBox(value = prettyNum(format(gcf_total,big.mark = ","), "", sep = ","),
         caption = "Total GCF Funding", 
         icon = "logo-usd", 
         color = "brown")
```


### GEF {.value-box} 


```{r}
gef_total<-sum(GEF_Grant$Grant)

valueBox(value = prettyNum(format(gef_total,big.mark = ","), "", sep = ","),
         caption = "Total GEF Funding", 
         icon = "logo-usd", 
         color = "orange")
```

### NAPs {.value-box} 

```{r}
naps_total<-count(NAPS)

valueBox(value = prettyNum(format(naps_total,big.mark = ","), "", sep = ","),
         caption = "NAPs Submitted", 
         icon = "document-text-outline", 
         color = "green")
```

Column { data-width=50 }
-----------------------------------------------------------------------

```{r}
library(ggplot2)
library(treemapify)
ggplot(data = GCF_Core, aes(fill=countryname, area=`Total GCF Funding`, label = paste(countryname,"\n",prettyNum(`Total GCF Funding`, big.mark = ","))))+
  geom_treemap()+
  geom_treemap_text(colour='black', place='centre')+
  labs(subtitle = 'GCF Projects Funding by Country')+
  theme(legend.position = 'none')
```

-----------------------------------------------------------------------

```{r}
library(ggplot2)
library(treemapify)
ggplot(data = subset(GEF_Grant,`Grant`>50000000), aes(fill=countryname, area=Grant, label = paste(countryname,"\n",prettyNum(Grant, big.mark = ","))))+
  geom_treemap()+
  geom_treemap_text(colour='black', place='centre')+
  labs(subtitle = 'GEF Project Grants by Country')+
  theme(legend.position = 'none')
 
```


Column { data-width=50 } 
-----------------------------------------------------------------------

```{r echo=FALSE}
library(ggplot2)
library(ggplotlyExtra)
library(treemapify)
ggplot(data = NAPA_df, aes(fill=countryname, area=NAPA_df$`Amount Approved`, label = paste(countryname,"\n",prettyNum(`Amount Approved`, big.mark = ","))))+
  geom_treemap()+
  geom_treemap_text(colour='black', place='centre')+
  labs(subtitle = 'NAPA Funding by Country')+
  theme(legend.position = 'none')
```


Readiness
========================================================================

### Projects by Region

```{r echo=FALSE, message=FALSE, warning=FALSE}
library(plotly)
library(dplyr)
library(base)
Country_Readiness %>% count(Country_Readiness$`Project Title`)%>%na.omit(Country_Readiness)%>%
   plot_ly(type = "bar", 
          y = Country_Readiness$`Project Title`, 
          x = Country_Readiness$Region)
```

### Amount Approved by Country

```{r,echo=FALSE}
library(plotly)
Readiness_df%>%arrange()%>%
   plot_ly(type = "bar", 
          x = Readiness_df$`Amount Approved`, 
          y = Readiness_df$countryname

                   )
```

```{r,echo=FALSE}
#library(plotly)
#Readiness_df %>% 
 #  plot_ly(type = "bar", 
  #        x = Readiness_df$`Amount Approved`, 
   #       y = ~Region,
    #      color = Readiness_df$`Grant Agreement Type` )

```
Outputs
========================================================================

### NAP Outputs

```{r, echo=FALSE, warning=FALSE,message=FALSE}
library(DT)
library(rio)

y<-napcountry_outputs[,c(2,5,7,12,11)]
datatable(y,filter = 'top',fillContainer = T,rownames = F, editable = F, style = 'foundation', class = 'display responsive', width = '100%', caption = "NAP Output Documents", extensions = 'Buttons', options=list(initComplete = JS("function(settings, json) {$(this.api().table().header()).css({'font-size' : '70%'});}"),pageLength= 10, dom='lfrtipB', buttons = c('copy', 'csv', 'excel', 'pdf')))%>%
  DT::formatStyle(columns = colnames(y),fontSize= '10px')

```

Map
========================================================================

### Countries with Submitted NAPs

```{r,echo=FALSE}
library(leaflet)
library(dplyr)
library(ggplot2)
library(tidyverse)
NAP_Countries<-NAP_Countries%>%
   mutate(popup_info=paste(countryname,"
","Submitted on:",NAP_Countries$`Date of submission`,"
","Link",NAP_Countries$Link))

leaflet(NAP_Countries)%>%
  addTiles()%>%
  addCircleMarkers(data=NAP_Countries, fill=T,lng=~lon, lat =~lat, radius = ~4,popup =~popup_info)
 
```

Temperature Time-series
========================================================================

```{r, include=FALSE,echo=FALSE}
#library(raster)
#library(dygraphs)
#temp<-getData('worldclim',var='tmean',res=2.5)
#df<-as.data.frame(temp, xy=TRUE)
#dygraph(df, main = "Wordclim Global Temperatures") %>%
 # dyRangeSelector(dateWindow = c("2020-01-01", "2100-12-01"))
```

```{r,echo=FALSE}
library(dygraphs)
dygraph(nhtemp, main = "New Haven Temperatures") %>% 
  dyRangeSelector(dateWindow = c("1920-01-01", "1980-12-01"))
```

About
========================================================================
### About

_The Open NAPs Dashboard_

Open NAPs are National Adaptation Plans for developing countries being developed through an open collaboration involving a wide range of actors, designed to support countries develop the best possible adaptation plans along the guidelines established by the UNFCCC Conference of Parties, and further elaborated through technical guidelines led by the LDC Expert Group (LEG), with continuing extension through supplements developed by a wide range of organizations.

The Open NAP initiative is moderated by the LEG under the UNFCCC. For more information about the work of the LEG, visit [here](https://unfccc.int/LEG)

For more information about the National Adaptation Plans, [browse here](https://napcentral.netlify.app/open-naps/)

For any questions or feedback, [write to us](opennaps@naps.org)